Sean joins us to chat about ML models and tools at Lyft Rideshare Labs, Python vs R, time series forecasting with Prophet, and election forecasting.

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Sean Taylor is a Data Scientist at (and former Head of) Lyft Rideshare Labs, and specializes in methods for solving causal inference and business decision problems. Previously, he was a Research Scientist on Facebook's Core Data Science team. His interests include experiments, causal inference, statistics, machine learning, and economics.

Connect with Sean:

Personal website: https://seanjtaylor.com/

Twitter: https://twitter.com/seanjtaylor

LinkedIn: https://www.linkedin.com/in/seanjtaylor/

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Topics Discussed:

0:00 Sneak peek, intro

0:50 Pricing algorithms at Lyft

07:46 Loss functions and ETAs at Lyft

12:59 Models and tools at Lyft

20:46 Python vs R

25:30 Forecasting time series data with Prophet

33:06 Election forecasting and prediction markets

40:55 Comparing and evaluating models

43:22 Bottlenecks in going from research to production

Transcript:

http://wandb.me/gd-sean-taylor

Links Discussed:

"How Lyft predicts a rider’s destination for better in-app experience"": https://eng.lyft.com/how-lyft-predicts-your-destination-with-attention-791146b0a439

Prophet: https://facebook.github.io/prophet/

Andrew Gelman's blog post "Facebook's Prophet uses Stan": https://statmodeling.stat.columbia.edu/2017/03/01/facebooks-prophet-uses-stan/

Twitter thread "Election forecasting using prediction markets": https://twitter.com/seanjtaylor/status/1270899371706466304

"An Updated Dynamic Bayesian Forecasting Model for the 2020 Election": https://hdsr.mitpress.mit.edu/pub/nw1dzd02/release/1

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